Overview
Yahboom AI Large Model Scene Sandbox Map is an ultra-large robotics training field designed for educational and competition scenarios. The arena uses a 4.1m × 3m (410 × 300CM) map to simulate factory and logistics environments for tasks such as handling/transport, sorting, inspection, parking navigation, and obstacle navigation. It supports use with wheeled robots, ROS platforms, Raspberry Pi/Jetson AI robots, robotic arms, and more.
Key Features
- Ultra-large high-definition map for multi-robot operation in labs, classrooms, and competitions.
- Scenario coverage includes simulated factory handling/inspection, SLAM road network navigation, and AI large model application.
- Functional areas can be customized; referenced zones include: Production workshop, Fire equipment area, Storage area, Parking spaces, Waste sorting area, Picking area, Loading and unloading area, Safety exits, Maintenance area, and high-contrast warning lines.
- Optional fence props kit forms a boundary around the arena to help keep robots within the field and reduce prop scattering during faster tasks.
For setup questions or replacement parts, contact support@rcdrone.top or visit https://rcdrone.top/.
Specifications
| Product | AI large model scenario sandbox map (L5) |
| Difficulty | L5 (★★★★★) |
| Principle | Simulated factory handling/inspection & SLAM road network navigation & AI large model application |
| Map size | 4.1m × 3m (410 × 300CM) |
| Map material | High-definition canvas; UV printing high-end oil painting cloth |
What's Included
AI large model scene sandbox (standard)
- AI large model map (4.1*3 meters), Material: High-definition canvas
- Trash cans (1 of each of four colors)
- Blue plastic mats *14
- Step shelves *4 sets
- Pallets (12 in total, four colors)
- Visual positioning codes *20
- Cube EVA blocks (40 in total, five colors)
- Rectangular EVA blocks (25 in total, five colors)
- Cylinder EVA blocks (25 in total, five colors)
- Small cube wooden blocks (4 in total)
- Cylinder wooden blocks (1 of each of the four colors)
- Rectangular wooden blocks (1 of each of the four colors)
- Large cube wooden block *1
Optional: AI large model scene sandbox + Fence Props
- 50 PCS Fence Props
- Snap-on*120
Note: The fence set includes 50 fence pieces in total with 120 fence clips; some displays may show only part of the fence pieces.
Applications
- Factory material transport and handling practice
- Waste sorting with 4-color bins
- Parking lot navigation (P1/P2 areas)
- Visual recognition challenges using visual positioning codes
- Patrol/inspection training (e.g., safety exit inspection, warehouse inventory check, shelf replenishment)
Video
Details

Choose the right map level for your curriculum—from basic line tracking to the L5 large-model factory scene.

Entry-level L1 track maps focus on straightforward infrared line-following practice.

L2 increases complexity for multi-sensor tracking exercises on a larger waterproof canvas.

L3 introduces visual recognition and traffic-sign training for more realistic indoor robot tasks.

L4 adds SLAM road-network navigation to bridge from tracking into autonomous navigation.

The L5 arena is built for simulated factory logistics, inspection routes, and SLAM navigation training at full scale.

A 4.1 m × 3 m field provides room for multi-robot drills, parking practice, and obstacle-aware navigation.

The standard kit includes the HD canvas map plus props for sorting, loading/unloading, and vision positioning challenges.

Add the optional fence set to define the boundary and help keep robots and props inside the training area.

Clear functional zones and high-contrast warning lines make it easy to assign repeatable tasks and evaluation points.

A complete overview of how the arena and included props combine into a modular factory-and-logistics training scene.

Typical lesson tasks include 4-color waste sorting and material transfer workflows using a mobile base and arm.

Picking, sorting, production, and loading areas support multi-step routes that mirror real warehouse operations.

Inventory checks and safety-exit inspection scenarios help teach structured patrol routines and exception handling.

Practice shelf replenishment as well as parking maneuvers in the marked P1/P2 bays.

SLAM mapping and goal-based navigation exercises can be run as step-by-step missions across the full arena.

Road-network patrols and handling tasks reinforce waypoint driving, pickup/drop-off, and route discipline.

Detailed dimensions help plan classroom space, boundary fencing, and prop placement for repeatable setups.

The kit includes four 3×3×3 cm wooden cube blocks with machine-code stickers and sorting-pattern decals for building training scenes.

The kit includes a 4×4×4 cm cube wooden block with a marker sticker and four 3×3 cm cylindrical wooden blocks in red, yellow, blue, and green.

The kit includes four 3×3×6 cm rectangular wooden blocks and forty 4×4×4 cm EVA cube blocks with machine-code stickers for robotics scene setup.

The set includes 25 cylindrical EVA foam blocks (φ4×4 cm, 2.4 g each) and 25 rectangular blocks (4×4×8 cm, 12 g each) in purple, green, blue, yellow, and red.

The kit includes 20 adhesive visual positioning codes and 14 blue perforated HDPE plates for building modular training layouts.

The kit includes four cardboard tiered racks with 12 removable trays and four color-coded trash cans for organizing sorting tasks.

The large sandbox map layout uses modular fence props to create a contained SLAM mapping and navigation practice area.

The single fence panel measures 45 cm by 35 cm for planning layout and spacing in your setup.

ABS octagonal snap buckles let the sandbox panels connect quickly for fast setup and reconfiguration.

The UV-printed canvas map is presented as waterproof and lightweight, with a comparison of material, print color, cost, and weight.

The Yahboom scene sandbox map kit creates a structured practice area for robot car navigation and object-sorting tasks.

The large sandbox scene includes marked navigation lanes and assorted obstacles like blocks, ramps, and sorting bins for SLAM and training setups.
